DirectML: All about the Artificial Intelligence revolution in Windows and gaming

In recent years, machine learning and Artificial Intelligence have taken on an increasingly prominent role in the world of technology, especially in the fields of gaming, creative software, and the Windows operating system. However, while terms such as DLSS, Ray Tracing, and acceleration by hardware They have been sounding strong for some time, there is a key piece that connects all this gear in the Microsoft ecosystem: DirectMLIf you've ever wondered what DirectML is, what it's used for, and how it's becoming the secret engine behind many of today's innovations, here's the most complete and up-to-date explanation.

DirectML isn't just a buzzword: it's a core technology designed to take full advantage of the modern capabilities of GPUs and NPUs for computing tasks. artificial intelligence and machine learning, whether in next-generation video games, apps multimedia content editing or advanced Windows functions. Today, understanding DirectML means understanding a good part of the technological revolution taking place in personal computers, consoles, and Windows devices.

What is DirectML and why is it important?

DirectML (Direct Machine Learning) is a low-level API created by Microsoft specifically to accelerate machine learning tasks on Windows systems through the DirectX 12 standard. This is a library that allows developers to harness the power of graphics cards (GPUs) and special-purpose processors (NPUs) to efficiently run artificial intelligence models. This API is designed not only for game development but also for all types of applications and engines, middleware, and environments that require advanced ML (machine learning) capabilities.

The reason for DirectML is to integrate and accelerate artificial intelligence into the Windows ecosystem.It allows complex algorithms to no longer rely exclusively on the central processing unit (CPU), but instead to take advantage of the enormous parallelism and computing power offered by modern GPUs and, more recently, the NPUs incorporated into many current systems. This makes tasks such as image processing, graphics enhancement, predictive model acceleration, and advanced task automation significantly faster, more efficient, and more accessible to more users and developers.

How does DirectML work in the Windows environment?

DirectML acts as a bridge between machine learning frameworks (such as TensorFlow or PyTorch) and DirectX 12-compatible hardware.. Its implementation is deeply integrated into Windows 10 since version 1903 (build 10.0.18362) and in Windows 11, making it easier for any compatible application to take advantage of this hardware acceleration.

The great advantage of DirectML is that it offers a consistent and simple API for developers, who can create and deploy AI inference models on a wide variety of devices and architectures.. It doesn't matter if we're talking about AMD GPUs, Intel, NVIDIA, Qualcomm, or specific NPUs: DirectML's goal is to unify calls and enable the maximum available performance. By using DirectX 12 as a foundation, you can squeeze the most out of your graphics hardware, with very low latency and unbeatable efficiency for tasks such as image supersampling, pattern recognition, or automatic multimedia classification.

In practical terms, DirectML has become the standard for accelerating AI frameworks on Windows.Microsoft promotes it as an integration channel, specifically for use with ONNX Runtime (a runtime environment for standard AI models), TensorFlow, and PyTorch, enabling applications and games to achieve vital improvements in speed and processing power without relying on specific hardware from a single brand.

Real-life use of DirectML: current applications and examples

Far from being a theoretical technology, DirectML is already used in numerous creative and professional solutions, especially in the new Windows Copilot+ PCs.These types of devices, equipped with advanced architectures with next-generation NPUs and GPUs, take full advantage of DirectML's capabilities to execute intelligent tasks locally and ultra-efficiently.

Among the applications that have already incorporated DirectML are some heavyweights in creative and productivity software:

• Adobe Premiere Pro: Uses DirectML for automatic audio clip tagging, instantly classifying each file based on its nature (music, dialogue, effects, ambience), which greatly speeds up editing.
• Capture One: has incorporated two AI-enhanced features with DirectML, such as Match Look and AI Crop, making it easier for photographers and creatives to work without losing precision.
• Affinity Photo 2: Leverages DirectML and the Qualcomm Hexagon NPU for automatic object and subject selection in photography, eliminating the need for manual masking and making editing much more efficient.

In the gaming sector, DirectML has been key to the research and development of techniques such as intelligent supersampling and improving graphics performance without sacrificing quality. For example, when Microsoft introduced the Xbox Series X|S, DirectML was identified by developers as one of the console’s greatest strengths, enabling radical performance improvements through techniques such as “ML super resolution,” art style transfer, and the development of emergent gameplay experiences.

DirectML's relationship with Ray Tracing, DLSS, and the competition

One of the most frequently asked questions is whether DirectML competes directly with technologies like NVIDIA's DLSS or if it is an alternative to Ray Tracing.The short answer is that DirectML and DLSS, while they share certain goals (AI-powered video game quality and performance improvements), are not exactly the same.

DLSS (Deep Learning Super Sampling) is a proprietary NVIDIA technology which uses deep learning to generate higher-resolution images from fewer GPU-rendered pixels, improving frame rates and visual quality. While only available for RTX cards, the concept behind DLSS is based on ML techniques that Microsoft is also looking to democratize through DirectML.

DirectML, on the other hand, is an open API integrated into Windows for any hardware compatible with DirectX 12. It allows developers to access a range of AI acceleration capabilities, from intelligent image upscaling (Super Resolution) to game engine optimization, texture generation, speech recognition, and automatic photo enhancement. As a result, DirectML can be used by AMD, Intel, and NVIDIA, opening the door to alternative solutions to DLSS on platforms like AMD GPUs with Radeon ML and similar technologies.

Ray tracing and AI also go hand in hand thanks to DirectMLWith the arrival of DirectX 12 Ultimate, technologies like DXR (DirectX Ray Tracing) and DirectML work together to give developers access to both realistic light ray simulation and graphics intelligence, generating more realistic, faster, and more efficient images.

For example, AMD has worked on solutions such as DirectML Super Resolution, enabling smart upscaling (upscaling a 540p image to a crisp 4K while reducing aliasing) on ​​its RX 6000 and Xbox cards, following an open, cross-platform philosophy that competes with DLSS but isn't limited to a single manufacturer.

DirectML integration into hardware: NPUs, GPUs, and the next generation of PCs

The most significant advancement in recent times is the integration of DirectML not only in GPUs but also in NPUs (neural processing units).Until now, DirectML was primarily used to take advantage of the power of graphics cards, both from AMD and NVIDIA and Intel. However, with the arrival of processors intel core ultra (Meteor Lake) and its AI Boost “NPU,” along with the Samsung alliance, DirectML has expanded to take full advantage of chips dedicated exclusively to AI tasks within the PC.

This is a revolution compared to the previous exclusive dependence on the GPU.. Now, tasks such as language model inference, real-time analysis, or multimedia classification can be outsourced to these NPUs, freeing up even more resources and improving power consumption. Support has initially reached portable from Intel and Samsung with Windows 11, requiring a combination of software requirements (DirectML 1.31.1, NuGet, ONNX Runtime 1.17 and drivers (updated Intel) to function properly.

While support for AMD's Ryzen AI is limited for now, it's clear that the industry is moving toward increasing DirectML integration across all types of hardware. Microsoft's vision is to ensure that no developer or user is left out of the AI ​​revolution, and in the short term, the new Ryzen 8040 processors are expected to include advanced support for DirectML and its NPU acceleration capabilities.

GPU Performance and Optimization Comparison: When is DirectML Worth It?

One of the most talked about topics about DirectML is its real-world performance compared to other native GPU solutions.. According to tests performed on different systems, for example using TensorFlow and DirectML on Windows Subsystem for Linux (WSL), it has been observed that There Execution time may vary significantly depending on the GPU and drivers used.

For example, on a system with a Ryzen 3600 processor and an AMD RX 6600 XT card, the test took approximately 139 seconds, while on an Nvidia RTX 3060 with native drivers the time was 97 seconds. This demonstrates that while DirectML is a powerful and flexible tool, there may still be situations where the raw performance of a native driver (especially on NVIDIA cards) is superior. However, the gap tends to narrow with each DirectML update and support expansion, especially on AMD hardware or in scenarios where cross-platform compatibility and access to intelligent features from standard AI frameworks are a priority.

It is not always a good idea to use DirectML, especially in these cases:

• If you have a very powerful GPU but no DirectML support, the native driver may offer better performance.
• For extremely complex and specific machine learning tasks, you may need more specialized solutions or libraries optimized directly for your hardware.
• If your development doesn't require AI or ML, DirectML offers no added value, and you can opt for other tools tailored to your workflow.

Current advantages and limitations of DirectML

Among the main benefits of DirectML are its full integration into Windows, compatibility with a wide range of hardware, and its ability to make AI accessible and efficient for any user or developer.. It allows you to accelerate both productivity-oriented applications and all types of video games, without requiring extensive knowledge of the hardware environment, as long as the system meets the minimum requirements for DirectX 12 and has adequate support.

In addition, DirectML is the channel through which developers can anticipate the future of local AI, avoiding exclusive dependence on the cloud or proprietary solutions.Thanks to its integration with technologies like ONNX Runtime, TensorFlow, PyTorch, and other popular AI libraries, the range of possibilities is as wide as the imagination of engineers and creatives.

However, there are still certain disadvantages or barriers to entry.:

• Performance in some cases still lags behind native drivers optimized for a specific GPU brand, although the gap is narrowing every year.
• NPU integration is still in its early stages, meaning only certain hardware models, versions, and drivers can take full advantage of these capabilities.
• There may be compatibility limitations for highly complex or specialized deep learning models, in which case it may be necessary to use alternative libraries.

Requirements, installation and startup of DirectML

To benefit from DirectML on Windows, you must first have a DirectX 12 compatible system and the appropriate version of the operating system (Windows 10 from build 1903 or Windows 11)From there, installation and use are usually tied to the software you'll be using (e.g., TensorFlow or PyTorch) and the corresponding GPU or NPU driver.

In advanced scenarios, such as using Windows Subsystem for Linux (WSL) and ML acceleration in Linux environments under Windows, the steps are typically as follows:

1. Enable the WSL subsystem and Virtual Machine Platform from Windows Features.
2. Install a Linux distribution (such as Ubuntu) from the Microsoft Store.
3. Set up a development environment like MiniConda for managing Python environments.
4. Install the appropriate version of TensorFlow or PyTorch and integrate DirectML support according to the official documentation.

For NPU acceleration, it is critical to have the latest version of DirectML, ONNX Runtime, and the most up-to-date drivers for the corresponding NPU (e.g., Intel AI Boost on Meteor Lake).Not all models or tasks are supported in early releases, so it's important to consult each hardware vendor's notes and lists of supported models.

The Future of DirectML: AI at the Heart of the Windows Ecosystem

Microsoft continues to invest heavily in DirectML as a driver of AI innovation for Windows.The development of Windows Copilot+, integration with new chips from Intel, Qualcomm, and AMD, and the opening of the platform to all types of developers predict an unstoppable expansion of DirectML in the coming years.

The DirectML team is working to add more features and make it easier for any creator to leverage AI in their applications., accelerating future innovations and democratizing the power of artificial intelligence on the desktop, web and mobile devicesThe trend is clear: more performance, fewer technical barriers, and an increasingly fluid and personalized experience for Windows users.

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Isaac

Passionate writer about the world of bytes and technology in general. I love sharing my knowledge through writing, and that's what I'll do on this blog, show you all the most interesting things about gadgets, software, hardware, tech trends, and more. My goal is to help you navigate the digital world in a simple and entertaining way.